Abstract: Current social and scientific endeavors are generating data that can be modeled as graphs: high-throughput biological experiments, screening of chemical compounds, social networks, ecological networks and food-webs, database schemas and ontologies. Access and analysis of such graphs is crucial for advancing the state of scientific research, accurate modeling and analysis of existing systems, and re-engineering of new systems. Traditional graph theory and most current research in graph modeling, querying, and mining concentrates on problems where the graph structure is inherently static and does not change with time. But networks in the real world are dynamic in nature with a wide range of temporal changes while the topology of networks such as social networks and transportation networks undergoes gradual change (or evolution), the content (information flow, annotations) changes more rapidly. A dynamic graph that comprises of a series of static graph snapshots has fundamentally different properties than the union of properties of constituent static graph snapshots. I will discuss techniques for mining significant dynamic subgraphs under different constraints of connectivity such as fixed subgraph structure, connected subgraphs, and smooth subgraphs. The goal is to find anomalous patterns in dynamic graph datasets using a statistical characterization of background behavior. I will also examine dynamic graphs from the point of view of content in order to better understand the relationship between content of a message and its flow in a network.

Speaker Profile: Ambuj Singh is a Professor of Computer Science and Biomolecular Science and Engineering at the University of California at Santa Barbara. He received his B.Tech. from the Indian Institute of Technology Kharagpur and PhD degree from the University of Texas at Austin in 1989. His research interests are in querying and mining large datasets, especially as they pertain to graphs and biological data. He has written over 180 technical papers in the areas of distributed computing, databases, and bioinformatics and graduated over 20 PhD students. He has led numerous interdisciplinary projects funded by the NSF, NIH, and other federal agencies.